Regular Data Engineer - Hybrid Environment (On-Prem & Cloud)

Project descriptionJoin our Development Center and become a member of our open-minded, progressive and professional team. You will have a chance to grow your technical and soft skills, and build a thorough expertise of the industry of our client. In this role you will be working on projects for one our world famous clients, a large international investment bank.On top of attractive salary and benefits package, Luxoft will invest into your professional training, and allow you to grow your professional career.ResponsibilitiesKey Responsibilities:Solution Design: Architect data pipelines down to the low-level elements, ensuring clarity and precision in implementation.Data Sourcing: Extract data from diverse repositories including relationaldatabases (Oracle, PostgreSQL), NoSQL stores, file systems, and otherstructured/unstructured sources.Data Transformation: Design and implement ETL/ELT workflows to standardize and cleanse data using best practices in data engineering.Pipeline Development: Build scalable, fault-tolerant data pipelines that support batch and streaming use cases.Cloud data processing: Load transformed data into GCP destinations such as BigQuery or Cloud Storage using tools like Dataproc, Dataflow, and other GCPnative services.Workflow Orchestration: Design and manage workflows using orchestration tools such as Apache Airflow or Cloud Composer.Data Format Expertise: Work with various data formats including JSON, AVRO, Parquet, CSV, and others.Optimization & Monitoring: Ensure performance, reliability, and cost-efficiency of data pipelines through continuous monitoring and tuning.Collaboration: Work closely with data architects, analysts, and businessstakeholders to understand data requirements and deliver high-quality solutions.SkillsMust haveRequired Skills & Experience:Experience in data engineering across hybrid environments (on-premise and cloud).Proficiency in SQL and Python or Java/Scala.Hands-on experience with ETL/ELT tools and frameworks.Good understanding of GCP data services: BigQuery, Dataproc, Dataflow, Cloud Storage.Familiarity with data modeling, schema design, and metadata management.Knowledge of data governance, security, and compliance best practices.Nice to havePreferred Qualifications:GCP certification (e.g., Professional Data Engineer) is a major plusExperience with CI/CD for data pipelines.Exposure to containerization and Kubernetes.Familiarity with data cataloging tools / metadata managementExperience with Big Data technologies